The attention mechanism enables graph neural networks (GNNs) to learn the attention weights between the target node and its one-hop neighbors, thereby improving the performance further. However, most existing GNNs are oriented toward homogeneous graphs, and in which each layer can only aggregate the information of one-hop neighbors. Stacking multilayer networks introduces considerable noise and easily leads to over smoothing. We propose here a multihop heterogeneous neighborhood information fusion graph representation learning method (MHNF). Specifically, we propose a hybrid metapath autonomous extraction model to efficiently extract multihop hybrid neighbors. Then, we formulate a hop-level heterogeneous information aggregation model, which selectively aggregates different-hop neighborhood information within the same hybrid metapath. Finally, a hierarchical semantic attention fusion model (HSAF) is constructed, which can efficiently integrate different-hop and different-path neighborhood information. In this fashion, this paper solves the problem of aggregating multihop neighborhood information and learning hybrid metapaths for target tasks. This mitigates the limitation of manually specifying metapaths. In addition, HSAF can extract the internal node information of the metapaths and better integrate the semantic information present at different levels. Experimental results on real datasets show that MHNF achieves the best or competitive performance against state-of-the-art baselines with only a fraction of 1/10 $\sim$ 1/100 parameters and computational budgets. Our code is publicly available at https://github.com/PHD-lanyu/MHNF

Seismic interpretation is a fundamental approach for obtaining static and dynamic information about subsurface reservoirs, such as geological faults/salt bodies and associated fluid types and distribution. Due to the exponential growth in seismic data volume and considerable uncertainty in manual interpretation, deep learning (DL) algorithms have been introduced to assist seismic interpretation. Our investigation of the trained neural networks suggests that they underperform on seismic data with different noise characteristics. One of the main issues is that the noise patterns of seismic data are highly inconsistent due to many factors, including geological features, sampling parameters and human intervention. To address this problem, we propose a noise pattern transfer (NPT) framework to transfer or remove seismic noise style between datasets by treating noise patterns as styles of image, which can also improve the generality of automatic seismic interpretation algorithms. Extensive experiments on three synthetic datasets and two field seismic datasets demonstrate the promising performance of our proposed NPT approach. Pairs of clean and stylised seismic data are generated by extending the use of the neural style transfer algorithm beyond the artistic domain. We then demonstrate how our method achieves superior noise pattern transferability between datasets and denoising performance on field datasets. Associated improvements in accuracy and generalisation of the neural network-based fault recognition tasks successfully demonstrate the practicality of our NPT approach. The source code is made publicly available online at https://github.com/Magnomic/npt-code.

The data consistency protocol is a core component of distributed data services that provide fault-tolerance and data consistency across distributed data centers and even edge networks. Raft is a popular approach due to its ease of implementation and superior performance. However, Raft adopts a sequential log entry processing strategy, where log entries without dependencies are not allowed to be processed in parallel, limiting system performance in high-concurrency scenarios. To address this challenge, researchers propose Raft-based protocols that support out-of-order apply (OOApply), which is called OORaft. Existing OORaft protocols adopt the Paxos-style election and replication process to merge missing entries on leader candidates. It leads to problems such as extra overhead on dependency analysis, availability when the network is partitioned, and incomplete correctness verification. This paper proposes a concise paralleled Raft protocol called CP-Raft, which is the first OORaft protocol to focus on dependency analysis overhead and to use full TLA$^+$ validation for the leader election process. Specifically, 1) CP-Raft proposes a Raft-aligned three-step election method that significantly simplifies the difficulty of understanding and solves the availability problem when the network is partitioned. 2) CP-Raft applies a leader-side bitmap-based dependency analysis and representation method to break through the performance bottleneck caused by the high overhead of dependency analysis. 3) CP-Raft discusses why existing methods cannot achieve OORaft correctness verification using TLA$^+$ in a limited time and uses phased verification methods to ensure its correctness. Finally, we implement CP-Raft based on an open-source Raft protocol and discuss its potential performance bottlenecks in various scenarios. The experimental results under high dependency strength workloads demonstrate that CP-Raft achieves 1.5× transaction per second (TPS) performance of DP-Raft and 2× of ParallelRaft-CE. It also provides better availability than state-of-the-art OORaft protocols.

Recently, leveraging different channels to model social semantic information and using self-supervised learning tasks to boost recommendation performance has been proven to be a very promising work. However, how to deeply dig out the relationship between different channels and make full use of it while maintaining the uniqueness of each channel is a problem that has not been well studied and resolved in this field. Under such circumstances, this paper explores and verifies the deficiency of directly constructing contrastive learning tasks on different channels with practical experiments and proposes the scheme of interactive modeling and matching representation across different channels. This is the first attempt in the field of recommender systems, we believe the insight of this paper is inspirational to future self-supervised learning research based on multi-channel information. To solve this problem, we propose a cross-channel matching representation model based on attentive interaction, which realizes efficient modeling of the relationship between cross-channel information. Based on this, we also propose a hierarchical self-supervised learning model, which realizes two levels of self-supervised learning within and between channels, which improves the ability of self-supervised tasks to autonomously mine different levels of potential information. We have conducted abundant experiments, and various metrics on multiple public datasets show that the method proposed in this paper has a significant improvement compared with the state-of-the-art methods, no matter in the general or cold-start scenario. And in the experiment of model variant analysis, the benefits of the cross-channel matching representation model and the hierarchical self-supervised model proposed in this paper are also fully verified.